Effect of Hyper-Parameter Optimization on the Deep Learning Model Proposed for Distributed Attack Detection in Internet of Things Environment
This paper studies the effect of various hyper-parameters and their selection for the best performance of the deep learning model proposed in [1] for distributed attack detection in the Internet of Things (IoT). The findings show that there are three hyper-parameters that have more influence on the best performance achieved by the model. As a consequence, this study shows that the model's accuracy as reported in the paper is not achievable, based on the best selections of parameters, which is also supported by another recent publication [2].
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Sparsity-Aware Distributed Learning for Gaussian Processes with Linear Multiple Kernel
Gaussian processes (GPs) stand as crucial tools in machine learning and signal processing, with their effectiveness hinging on kernel design and hyper-parameter optimization. This paper presents a novel GP linear multipl…
Gaussian ProcessesMetaoptimization on a Distributed System for Deep Reinforcement Learning
Training intelligent agents through reinforcement learning is a notoriously unstable procedure. Massive parallelization on GPUs and distributed systems has been exploited to generate a large amount of training experience…
Atari GamesDeep Reinforcement Learningreinforcement-learningReinforcement Learning+1SHADHO: Massively Scalable Hardware-Aware Distributed Hyperparameter Optimization
Computer vision is experiencing an AI renaissance, in which machine learning models are expediting important breakthroughs in academic research and commercial applications. Effectively training these models, however, is …
Cell SegmentationHyperparameter OptimizationA Practical Second-order Latent Factor Model via Distributed Particle Swarm Optimization
Latent Factor (LF) models are effective in representing high-dimension and sparse (HiDS) data via low-rank matrices approximation. Hessian-free (HF) optimization is an efficient method to utilizing second-order informati…
Hierarchical Collaborative Hyper-parameter Tuning
Hyper-parameter Tuning is among the most critical stages in building machine learning solutions. This paper demonstrates how multi-agent systems can be utilized to develop a distributed technique for determining near-opt…
BIG-bench Machine Learning